 Normal mean and variance maximum-likelihood estimators

ID: normal-mean-and-variance-maximum-likelihood-estimators

X_{1}, \dots, X_{n} \sim N (μ, σ^{2})

μ = \overline{X}, σ^{2} = \frac{1}{n} \sum_{i = 1}^{n} (X_{i} - \overline{X})^{2} .

The first is unbiased, while

E σ^{2} = (n - 1) σ^{2} / n

 New to topics? Read the docs here!